Towards Deep Learning Models Resistant to Adversarial Attacks.

Articles citing Towards Deep Learning Models Resistant to Adversarial Attacks.

Title / Author	Year
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples A Athalye, N Carlini, D Wagner arXiv preprint arXiv:1802.00420	2018
Ensemble adversarial training: Attacks and defenses F Tramèr, A Kurakin, N Papernot, I Goodfellow, D Boneh, P McDaniel arXiv preprint arXiv:1705.07204	2017
Synthesizing robust adversarial examples A Athalye, L Engstrom, A Ilyas, K Kwok International conference on machine learning, 284-293	2018
Feature squeezing: Detecting adversarial examples in deep neural networks W Xu, D Evans, Y Qi arXiv preprint arXiv:1704.01155	2017
Threat of adversarial attacks on deep learning in computer vision: A survey N Akhtar, A Mian IEEE Access 6, 14410-14430	2018
Deep reinforcement learning: An overview Y Li arXiv preprint arXiv:1701.07274	2017
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models PY Chen, H Zhang, Y Sharma, J Yi, CJ Hsieh Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security …	2017
Audio adversarial examples: Targeted attacks on speech-to-text N Carlini, D Wagner 2018 IEEE Security and Privacy Workshops (SPW), 1-7	2018
Provable defenses against adversarial examples via the convex outer adversarial polytope E Wong, Z Kolter International Conference on Machine Learning, 5286-5295	2018
Wild patterns: Ten years after the rise of adversarial machine learning B Biggio, F Roli Pattern Recognition 84, 317-331	2018
Robust physical-world attacks on machine learning models I Evtimov, K Eykholt, E Fernandes, T Kohno, B Li, A Prakash, A Rahmati, ... arXiv preprint arXiv:1707.08945 2 (3), 4	2017
Pixeldefend: Leveraging generative models to understand and defend against adversarial examples Y Song, T Kim, S Nowozin, S Ermon, N Kushman arXiv preprint arXiv:1710.10766	2017
Robustness may be at odds with accuracy D Tsipras, S Santurkar, L Engstrom, A Turner, A Madry arXiv preprint arXiv:1805.12152	2018
Ai2: Safety and robustness certification of neural networks with abstract interpretation T Gehr, M Mirman, D Drachsler-Cohen, P Tsankov, S Chaudhuri, ... 2018 IEEE Symposium on Security and Privacy (SP), 3-18	2018
Certifying some distributional robustness with principled adversarial training A Sinha, H Namkoong, J Duchi arXiv preprint arXiv:1710.10571	2017
Explaining explanations: An overview of interpretability of machine learning LH Gilpin, D Bau, BZ Yuan, A Bajwa, M Specter, L Kagal 2018 IEEE 5th International Conference on data science and advanced …	2018
Adversarially robust generalization requires more data L Schmidt, S Santurkar, D Tsipras, K Talwar, A Madry Advances in Neural Information Processing Systems, 5014-5026	2018
Ead: elastic-net attacks to deep neural networks via adversarial examples PY Chen, Y Sharma, H Zhang, J Yi, CJ Hsieh Thirty-second AAAI conference on artificial intelligence	2018
Theoretically principled trade-off between robustness and accuracy H Zhang, Y Yu, J Jiao, EP Xing, LE Ghaoui, MI Jordan arXiv preprint arXiv:1901.08573	2019
Certified adversarial robustness via randomized smoothing JM Cohen, E Rosenfeld, JZ Kolter arXiv preprint arXiv:1902.02918	2019

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